## Summary
Enhances `ExecutionInfo` and `Runtime` to surface richer execution
context and introduces `ServerInfo` for LangGraph Server metadata.
### `ExecutionInfo` expansion
Converted from `NamedTuple` to a frozen `dataclass`. Added identity
fields populated during task preparation in `_algo.py`:
| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `checkpoint_id` | `str` | required | Checkpoint ID for the current
execution |
| `checkpoint_ns` | `str` | required | Checkpoint namespace for the
current execution |
| `task_id` | `str` | required | Task ID for the current execution |
| `thread_id` | `str \| None` | `None` | Thread ID (None without a
checkpointer) |
| `run_id` | `str \| None` | `None` | Run ID (None when not provided in
config) |
| `node_attempt` | `int` | `1` | Current node execution attempt number
(1-indexed) |
| `node_first_attempt_time` | `float \| None` | `None` | Unix timestamp
for when the first attempt started |
`checkpoint_id`, `checkpoint_ns`, and `task_id` are required (no
defaults) — they are always populated during task preparation in
`_algo.py`. `Runtime.execution_info` is `None` until that point.
### New `ServerInfo` type
Frozen dataclass with `assistant_id: str`, `graph_id: str`, and optional
`user: BaseUser | None`. Populated from config metadata (`assistant_id`,
`graph_id`) and `configurable["langgraph_auth_user"]` via
`_build_server_info()` in `pregel/main.py`.
User detection uses `isinstance(BaseUser)` with a `hasattr("identity")`
fallback — needed because the server's `ProxyUser` provides
`permissions` via `__getattr__`, which Python's `runtime_checkable`
Protocol check doesn't see.
### `Runtime` changes
- `execution_info` is now `ExecutionInfo | None` (default `None`), set
during task prep
- Added `server_info: ServerInfo | None` field, wired through `merge()`
and `override()`
### `ToolNode` / `ToolRuntime` forwarding
`execution_info` and `server_info` are forwarded from `Runtime` to
`ToolRuntime` so tools can access execution and server context.
### New public API surface
```python
from langgraph.runtime import BaseUser, ExecutionInfo, Runtime, ServerInfo, get_runtime
```
## Test plan
- [x] `ExecutionInfo` defaults, patch, and frozen behavior
- [x] Integration tests verifying identity fields are populated in sync
and async execution
- [x] Retry tests confirming identity fields persist and `node_attempt`
increments
- [x] `ServerInfo` construction, frozen behavior, and `Runtime.merge`
precedence
- [x] `server_info` populated from config metadata and
`langgraph_auth_user` (including starlette-style proxy user)
- [x] `server_info` is `None` when no server metadata present
- [x] `ToolRuntime` forwarding of `execution_info` and `server_info`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
ensuring that injected args can only be injected by LC code, not LLMs :)
this does not appear to be an actual security concern because it falls
outside of documented usage, the tool node doesn't inject arbitrary args
**Description:**
This PR fixes an issue where injection types (like `ToolRuntime`) were
not recognized by `ToolNode` when used with generic type arguments
(e.g., `ToolRuntime[MyContext]`).
Previously, the `_is_injection` check relied solely on `isinstance` and
`issubclass`, which fail for `typing._GenericAlias` objects. This update
adds a check using `typing.get_origin()` to correctly identify the base
class of generic types, ensuring the runtime is injected correctly even
when type hints are present.
**Issue:** Fixes#6465
**Dependencies:** None
**Twitter handle:** @SidharthRajmoh2
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Description: Catch invalid checkpointer objects early by validating any
checkpointer argument before compilation/execution, raising a clear
TypeError that instructs users to pass a proper BaseCheckpointSaver
(e.g., AsyncPostgresSaver) instead of stores like AsyncPostgresStore.
Includes shared validation logic and a regression test so we don’t see
AttributeError: 'AsyncPostgresStore' object has no attribute
'get_next_version' again.
Issue: Fixes#6585
Dependencies: None
Twitter handle: none
This PR improves the consistency of interrupt streaming.
- when streaming with stream_mode values, the stream chunk now contains
the entire state alongside the interrupt:
```python
class State(TypedDict):
robot_input: str
# at this point in time robot_input is already set to "beep boop i am a robot"
app.stream(..., stream_mode="values")
# before
{"__interrupt__": (Interrupt(value="interrupt",))}}
# after
{"robot_input": "beep boop i am a robot", "__interrupt__": (Interrupt(value="interrupt"))}
```
- when streaming with stream_mode=["values", "updates"], interrupts are
surfaced in both an update stream chunk and the value stream chunk, when
previously we keep interrupt in values only if we request values mode
only
```python
class State(TypedDict):
robot_input: str
# at this point in time robot_input is already set to "beep boop i am a robot"
app.stream(..., stream_mode=["values", "updates"])
# before (interrupt would only emit on update chunk, there would be no values chunk)
("updates", {"__interrupt__": (Interrupt(value="interrupt",))}})
# after
("updates", {"__interrupt__": (Interrupt(value="interrupt",))}})
("values", {"robot_input": "beep boop i am a robot", "__interrupt__": (Interrupt(value="interrupt"))})
```
For housekeeping: this PR improves on this revert:
https://github.com/langchain-ai/langgraph/pull/6141
## overview
The main purpose of this is to respect tool signatures that request
injected args (like `ToolRuntime`) even when the explicitly specified
`args_schema` does not.
Ex in the following example, we should still inject `runtime` despite
its absence in `ArgsSchema`
```py
class ArgsSchema(BaseModel):
some_arg: int = Field(...)
@tool(args_schema=ArgsSchema)
def my_tool(some_arg: int, runtime: ToolRuntime): ...
```
This is accompanied by
https://github.com/langchain-ai/langchain/pull/34051 which has tests
that pass w/ this change. This tests injection w/ `create_agent` (more
end to end than tests added in
https://github.com/langchain-ai/langchain/pull/33999.
This unblocks the injection of `ToolRuntime` into MCP tools which is
exciting bc that exposes tool call id and state, which we previously
were unable to do.
## other benefits
* Cleaner code structure w/ more helpful docs about injected args.
* Nice perf boost, we're no longer inspecting the annotations of a
tool's schema 3 different times to detect store, state, and runtime
injections.
## additional notes
1. I could see a world where we want more of this logic to reside on the
tools themselves, but tools don't now about LG specific injection types
(like `ToolRuntime`, hence having this logic here for now).
2. We could separately add validation for the case where something is
specified in `args_schema` and not in the function signature (probably
at the tool level though).
namespace decisions
```
langgraph.prebuilt
├── ToolRuntime # new
# all of the other stuff that was already there
langgraph.prebuilt.tool_node
├── ToolNode
├── ToolCallRequest # new
├── ToolRuntime # new
├── InjectedState
├── InjectedStore
├── ToolCallWrapper
├── AsyncToolCallWrapper
├── tools_condition
```
```
langchain.tools
├── ToolRuntime # now from langgraph.prebuilt
├── InjectedState # now from langgraph.prebuilt
├── InjectedStore # now from langgraph.prebuilt
├── ToolException
├── tool
├── BaseTool
├── InjectedToolArg
├── InjectedToolCallId
```
* catching error thrown by asyncio
* using 2nd check for annotations given Pydantic 2.12 changes
* skipping tests for remote graph bc langgraph-api is dependent on
`jsonschema-rs`
* skipping tests w/ pydantic v1 models
```bash
hint: This usually indicates a problem with the package or the build environment.
help: `jsonschema-rs` (v0.29.1) was included because `langgraph:dev` (v1.0.0rc1) depends on `langgraph-cli[inmem]` which
depends on `langgraph-api` (v0.4.29) which depends on `jsonschema-rs`
```
not yet testing for free threaded python, that'll be much more involved!
ended up separating lint / testing deps during this process bc I was
getting a ton of not required deps while testing that were complicating
things :/
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
To make tests pass:
* linting fixes
* whitespace fixes in snapshots
---------
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
### Description
Adds Redis as a supported cache backend for LangGraph node-level
caching, enabling distributed caching across multiple processes/servers.
This implementation follows the same patterns as existing InMemoryCache
and SqliteCache.
### Key changes
- New RedisCache class implementing the BaseCache interface
- Support for TTL-based expiration and batch operations
- Worker-specific cache prefixes for parallel test isolation
### Dependencies
- redis package (already included in dev dependencies)
### Test Plan
- Unit tests: Added Redis cache tests covering basic operations, TTL,
batch operations, and error handling
- Integration tests: Redis cache integrated into existing LangGraph test
suite, tested with all checkpointer combinations
Reproduces:
https://github.com/langchain-ai/langgraph/issues/5249#issuecomment-3156519635
Caused after this change:
https://github.com/langchain-ai/langgraph/pull/4843
Fix to allow emitting messages from subgraphs if the subgraphs
explicitly used a stream mode "messages".
```python
def node_in_parent(...):
# subgraph was called as a function.
# messages are explicitly requested.
for event in subgraph.stream(..., stream_mode="messages"):
# something is done with `event`
return ...
# subgraphs = False!
parent_graph.invoke(..., subgraphs=False)
```
The code above should continue to work correctly regardless of the value
of subgraphs as streaming messages was requested explicitly in the
parent node!
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
## Description
Previously, when a tool returned `Command` to update the graph's state,
the `_validate_tool_command` method in `ToolNode` would raise a
`ValueError` if the `messages_update` list contained only a
`RemoveMessage(id=REMOVE_ALL_MESSAGES)` object. This was because the
validation logic expected a matching `ToolMessage` for the tool call and
did not account for this specific state-clearing scenario.
This commit modifies the validation logic to check if the
`messages_update` list contains a single
`RemoveMessage(id=REMOVE_ALL_MESSAGES)` element. If this condition is
met, the `ToolMessage` validation is bypassed, allowing a tool to clear
the entire message history without causing a validation error.
A new test case, `test_tool_node_command_remove_all_messages`, has been
added to `tests/test_tool_node.py` to verify this change and prevent
future regressions.
## Example
Here is a self-contained example that illustrates the problem and the
fix. Without this change, the code block for `Example 2` would raise a
`ValueError`.
```python
from typing import Annotated, List
from langchain_core.messages import (
AIMessage,
AnyMessage,
HumanMessage,
RemoveMessage,
ToolMessage,
)
from langchain_core.tools import InjectedToolCallId, tool
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, add_messages
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.prebuilt import InjectedState, ToolNode
from langgraph.types import Command
from pydantic import BaseModel, Field
# Agent state tracks current and all messages
class AgentState(BaseModel):
messages: Annotated[List[AnyMessage], add_messages] = Field(
default_factory=list, description="Current conversation messages."
)
all_messages: Annotated[List[AnyMessage], add_messages] = Field(
default_factory=list, description="All messages, including removed ones."
)
# Tool to clear history if long enough, otherwise returns a warning
@tool
def clear_history_tool(
state: Annotated[AgentState, InjectedState],
tool_call_id: Annotated[str, InjectedToolCallId],
):
"""Clears message history if it's long enough."""
if len(state.messages) < 3:
return Command(
update={
"messages": [
ToolMessage(
"History is not long enough to be cleared. Please try again.",
tool_call_id=tool_call_id,
)
]
}
)
else:
return Command(
update={
"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)],
"all_messages": state.messages
+ [
ToolMessage(
"History has been successfully cleared.",
tool_call_id=tool_call_id,
)
],
}
)
# Bind the tool to the model
model = ChatOpenAI(model="gpt-4o-mini").bind_tools([clear_history_tool])
def model_node(state: AgentState):
return {"messages": [model.invoke(state.messages)]}
# Build the agent graph
graph_builder = StateGraph(AgentState)
graph_builder.add_node("model", model_node)
graph_builder.add_node("tools", ToolNode([clear_history_tool]))
graph_builder.set_entry_point("model")
graph_builder.add_edge("model", "tools")
graph_builder.add_edge("tools", END)
graph = graph_builder.compile()
def print_messages(header, messages):
print(f"\n{header}")
for message in messages:
message.pretty_print()
### Example 1: Not enough history to clear
state_1 = AgentState(
messages=[HumanMessage(content="Please clear my message history.")]
)
output_1 = graph.invoke(state_1)
print_messages("First call: State 'messages'", output_1["messages"])
print_messages("First call: State 'all_messages'", output_1["all_messages"])
### Example 2: History is cleared
state_2 = AgentState(
messages=[
HumanMessage(content="Will this PR get merged?"),
AIMessage(content="Maybe, if it's good enough."),
HumanMessage(content="Please clear my message history."),
]
)
# Without the changes in this PR, the following line will raise a ValueError
output_2 = graph.invoke(state_2)
print_messages("Second call: State 'messages'", output_2["messages"])
print_messages("Second call: State 'all_messages'", output_2["all_messages"])
```
### Outputs
*Without the changes in this PR:*
```
First call: State 'messages'
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (ba421ac3-1e1a-4208-a8f6-c5500ee0abcc)
Call ID: ba421ac3-1e1a-4208-a8f6-c5500ee0abcc
Args:
================================= Tool Message =================================
Name: clear_history_tool
History is not long enough to be cleared. Please try again.
First call: State 'all_messages'
Traceback (most recent call last):
File "main.py", line 114, in <module>
output_2 = graph.invoke(state_2)
^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 2844, in invoke
for chunk in self.stream(
File ".venv/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 2534, in stream
for _ in runner.tick(
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 241, in _func
outputs = [
^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 619, in result_iterator
yield _result_or_cancel(fs.pop())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 317, in _result_or_cancel
return fut.result(timeout)
^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File ".venv/lib/python3.11/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain_core/runnables/config.py", line 555, in _wrapped_fn
return contexts.pop().run(fn, *args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 353, in _run_one
return self._validate_tool_command(response, call, input_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 616, in _validate_tool_command
raise ValueError(
ValueError: Expected to have a matching ToolMessage in Command.update for tool 'clear_history_tool', got: [RemoveMessage(content='', additional_kwargs={}, response_metadata={}, id='__remove_all__')]. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage. You can fix it by modifying the tool to return `Command(update={"messages": [ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`.
```
*With the changes in this PR:*
```
First call: State 'messages'
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (ba421ac3-1e1a-4208-a8f6-c5500ee0abcc)
Call ID: ba421ac3-1e1a-4208-a8f6-c5500ee0abcc
Args:
================================= Tool Message =================================
Name: clear_history_tool
History is not long enough to be cleared. Please try again.
First call: State 'all_messages'
Second call: State 'messages'
Second call: State 'all_messages'
================================ Human Message =================================
Will this PR get merged?
================================== Ai Message ==================================
Maybe, if it's good enough.
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (499b1be3-6df1-493f-85e5-8d7e429dead8)
Call ID: 499b1be3-6df1-493f-85e5-8d7e429dead8
Args:
================================= Tool Message =================================
Name: clear_history_tool
History has been successfully cleared.
```
## Twitter handle
[@samuelpullely](https://x.com/samuelpullely)
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR allows a developer to change the model configuration at run time based on context. This includes that list of tools available to the model to call.
```python
def create_react_agent(
model: Union[
str,
LanguageModelLike,
Callable[[SateLike, Runtime...], BaseChatModel], # <--- New
],
tools: Union[
Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode]
],
*,
....
llm = init_chat_model(...)
def prepare_model(state, runtime):
selected_tool_names = func(state, context)
return llm.bind(tools=selected_tool_names)
create_react_agent(
prepare_model,
tools=all_known_tools
)
```
## Semantics
1. `tools` = are the known tools, used to configure ToolNode and will
configure:
1. model provided as string
2. model provided as BaseChatModel (if it has no tools bound to it)
2. If a user provides a dynamic model (callable), the user is
responsible for binding tools
Alternative considered:
1. Passing `Callable[[SateLike, Config...], list[BaseTool]]` to tools
2. Passing `Callable[[SateLike, Config...], list[str]]` to a tool
selector
Both have the issue that there's non obvious interplay between tool
selection and dynamic models. (i.e., if we want to introduce dynamic
models at in the future, the API will become tricky to explain)
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
* Cleaning up the underlying tool injection logic which is happening in
multiple locations.
* State was being injected into the ToolCall via Send in two places in
create react agent and the logic doesn't belong there, the actual
injection should be happening inside the ToolNode where there's
awareness of what run time parameters the tool accepts.
Change is required to unblock:
https://github.com/langchain-ai/langgraph/pull/5537
Fixes#5554
This PR removes unused utility classes and functions from the prebuilt
tests directory to clean up dead code.
Changes include:
- Removed unused classes from `libs/prebuilt/tests/any_str.py`:
- Deleted FloatBetween, AnyDict, AnyVersion, and UnsortedSequence
- Kept only AnyStr class
- Removed unused functions from `libs/prebuilt/tests/messages.py`:
- Deleted _AnyIdDocument and _AnyIdAIMessageChunk
- Kept _AnyIdHumanMessage and _AnyIdToolMessage
- Removed unused classes from `libs/prebuilt/tests/memory_assert.py`:
- Deleted NoopSerializer, MemorySaverAssertCheckpointMetadata, and
MemorySaverNoPending
- Kept MemorySaverAssertImmutable
Verification:
- Manually checked for no remaining references to removed code
- Maintained existing import structures
- Preserved functionality of the prebuilt test suite
The changes reduce code complexity and remove unnecessary utility
classes that were not being used in the test suite.
---------
Co-authored-by: open-swe-dev[bot] <open-swe-dev@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
* public interfaces for channels
* public interfaces for func
* public interfaces for graph
* pi for managed
* first pass public interface for top level modules
* first pass at private for utils -> _internal
* private interface for pregel
* scratchpad/stream protocol move
* docs update
* backwards compat for runnable
* deprecation warning for send and interrupt
* deprecation for pregel import
- Leave it up to each checkpointer implementation to decide whether to merge in configurable/metadata (previously PregelLoop would do some of this always)
- Never copy over internal langgraph keys into checkpoint.metadata (these are redundant/misleading to include)
Prepare langgraph-checkpoint for 0.5
- Given we have no upper bound on langgraph-checkpoint dep need to undo all changes in langgraph-checkpoint that might break previous versions of langgraph
- This has been superseded by saving the individual writes of each task through put_writes()
- Removing this speeds up checkpoint operations as it was duplicating data saved elsewhere already
- Now all tests fully migrated to more recent sync_checkpointer and async_checkpointer fixtures for parametrising on checkpointer
- Use sync/async_store fixtures where tests used only in memory store
- Remove unused "should snapshot" check for older versions of langchain core no longer tested against
* Migrate to `uv`
* Format `pyproject.toml` files properly
* Remove upper bounds on dependencies, and bounds on dev dependencies
(we should be using latest)
* Move to hatch for packaing
In the future we should:
* Set up dependabot / automate lockfile updates and tests
* Add tests for min compatible versions (I'll do this right after merge)
* Use dynamic versioning
* Bump `pydantic` to v2.11.4 in the lockfile, we have some tests failing
Inherited attributes where not considered.
Pydantic model can inherit from other pydantic models. In those cases,
inherited attributes where not considered in the check and the code
fails.
---------
Co-authored-by: vbarda <vadym@langchain.dev>
